Move test_xor_original_1986.py to tests/integration/

test_xor_original_1986.py

@@ -1,95 +0,0 @@
-#!/usr/bin/env python3
-"""
-Original 1986 XOR Solution - Rumelhart, Hinton, Williams
-Testing the MINIMAL architecture that solved the XOR crisis.
-"""
-import sys
-sys.path.insert(0, '.')
-
-import numpy as np
-from tinytorch import Tensor, Linear, Sigmoid, BinaryCrossEntropyLoss, SGD
-
-print("=" * 70)
-print("🏛️  ORIGINAL 1986 XOR SOLUTION")
-print("Rumelhart, Hinton, Williams - 'Learning representations by back-propagating errors'")
-print("=" * 70)
-
-# Pure XOR
-X_data = np.array([[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]], dtype=np.float32)
-y_data = np.array([[0.0], [1.0], [1.0], [0.0]], dtype=np.float32)
-
-X = Tensor(X_data)
-y = Tensor(y_data)
-
-print("\n🏗️  Architecture (1986 style):")
-print("  Input: 2 neurons")
-print("  Hidden: 2 neurons (MINIMAL!)")
-print("  Output: 1 neuron")
-print("  Activation: Sigmoid (ReLU didn't exist yet!)")
-print("  Total params: 9 (2×2 weights + 2 bias + 2×1 weights + 1 bias)")
-
-# Original architecture: 2-2-1 with Sigmoid
-hidden = Linear(2, 2)  # Only 2 hidden neurons!
-sigmoid_hidden = Sigmoid()
-output = Linear(2, 1)
-sigmoid_output = Sigmoid()
-
-loss_fn = BinaryCrossEntropyLoss()
-optimizer = SGD([p for p in hidden.parameters()] + [p for p in output.parameters()], lr=1.0)
-
-print("\n🔥 Training with original 1986 architecture...")
-epochs = 2000  # May need more epochs with only 2 hidden units
-
-for epoch in range(epochs):
-    # Forward (all sigmoid, like 1986!)
-    h = hidden(X)
-    h_act = sigmoid_hidden(h)  # Sigmoid in hidden layer
-    out = output(h_act)
-    pred = sigmoid_output(out)  # Sigmoid in output layer
-    loss = loss_fn(pred, y)
-    
-    # Backward
-    loss.backward()
-    
-    # Update
-    optimizer.step()
-    optimizer.zero_grad()
-    
-    if (epoch + 1) % 400 == 0:
-        accuracy = ((pred.data > 0.5).astype(float) == y.data).mean()
-        print(f"Epoch {epoch+1:4d}/{epochs}  Loss: {loss.data:.4f}  Accuracy: {accuracy:.1%}")
-
-# Final evaluation
-print("\n✅ Final Results:")
-final_accuracy = ((pred.data > 0.5).astype(float) == y.data).mean()
-
-for i in range(4):
-    x_in = X_data[i]
-    y_true = int(y_data[i, 0])
-    y_pred_prob = pred.data[i, 0]
-    y_pred = int(y_pred_prob > 0.5)
-    status = "✅" if y_pred == y_true else "❌"
-    print(f"  Input: {x_in}  →  Pred: {y_pred} (prob: {y_pred_prob:.3f})  True: {y_true}  {status}")
-
-print(f"\n📊 Final Accuracy: {final_accuracy:.1%}")
-print(f"📊 Final Loss: {loss.data:.4f}")
-
-if final_accuracy == 1.0:
-    print("\n🎉 SUCCESS! XOR solved with MINIMAL 1986 architecture!")
-    print("   This is exactly what ended the AI Winter!")
-else:
-    print(f"\n⚠️  Accuracy: {final_accuracy:.1%} - may need more training")
-
-# Show what the hidden units learned
-print("\n🧠 What the 2 hidden neurons learned:")
-print("   (Examining activation patterns)")
-h_activations = sigmoid_hidden(hidden(X)).data
-print(f"\n   Hidden unit activations for each input:")
-for i, x_in in enumerate(X_data):
-    print(f"   {x_in}: h1={h_activations[i,0]:.3f}, h2={h_activations[i,1]:.3f}")
-
-print("\n" + "=" * 70)
-print("💡 Historical Note:")
-print("   This 2-2-1 architecture ended the 17-year AI Winter!")
-print("   Proved that backprop + hidden layers solve 'impossible' problems")
-print("=" * 70)